System and method for processing multimodal images

ABSTRACT

Various aspects of a system and a method to process multimodal images are disclosed herein. In accordance with an embodiment, the system includes an image-processing device that generates a structured point cloud, which represents edge points of an anatomical portion. The structured point cloud is generated based on shrink-wrapping of an unstructured point cloud to a boundary of the anatomical portion. Diffusion filtering is performed to dilate edge points that correspond to the structured point cloud to mutually connect the edge points on the structured point cloud. A mask is created for the anatomical portion based on the diffusion filtering.

REFERENCE

None.

FIELD

Various embodiments of the disclosure relate to processing of multimodalimages. More specifically, various embodiments of the disclosure relateto processing of multimodal images associated with an anatomical portionof a subject.

BACKGROUND

Advancements in the field of medical imaging techniques and associatedsensors or devices have made possible to visualize the interior of abody for clinical analysis and medical purposes. Different modalities,such a Computerized Tomography (CT) scanner and Magnetic ResonanceImaging (MRI) machines, provide different types of medical images for ananatomical portion-of-interest. Such different types of images arereferred to as multimodal images. Multimodal images of the sameanatomical portion, such as a skull portion, of the same subject mayprovide different visual representations and varied informationdepending on the modality used. It may be difficult to register suchmultimodal images because of different characteristics, such asstructural, resolution, and/or clinical usage differences of thedifferent imaging sensors. The multimodal images also have at least somecommon information content, which if located and computed accurately,registration may be achieved even for the multimodal images obtainedfrom different sensors. Thus, an advanced technique and/or system may berequired to process such multimodal images to generate enhancedvisualization of one or more anatomical portions of a particular subjectwith improved accuracy. Such enhanced visualization may be employed byusers, such as a physician, for diagnostic purposes and/or for provisionof assistance in surgery.

Further limitations and disadvantages of conventional and traditionalapproaches will become apparent to one of skill in the art, throughcomparison of described systems with some aspects of the presentdisclosure, as set forth in the remainder of the present application andwith reference to the drawings.

SUMMARY

A method and a system are provided to process multimodal imagessubstantially as shown in, and/or described in connection with, at leastone of the figures, as set forth more completely in the claims.

These and other features and advantages of the present disclosure may beappreciated from a review of the following detailed description of thepresent disclosure, along with the accompanying figures in which likereference numerals refer to like parts throughout.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram that illustrates a network environment toprocess multimodal images, in accordance with an embodiment of thedisclosure.

FIG. 2 illustrates a block diagram of an exemplary image-processingdevice to process multimodal images, in accordance with an embodiment ofthe disclosure.

FIGS. 3A to 3F, collectively, illustrate an exemplary scenario forimplementation of the system and method to process multimodal images, inaccordance with an embodiment of the disclosure.

FIG. 4 illustrates a flow chart for implementation of an exemplarymethod to process multimodal images, in accordance with an embodiment ofthe disclosure.

DETAILED DESCRIPTION

The following described implementations may be found in the disclosedsystem and method to process multimodal images. Exemplary aspects of thedisclosure may include generation of a structured point cloud by animage-processing device that represents edge points of an anatomicalportion. The structured point cloud may be generated based onshrink-wrapping of an unstructured point cloud to a boundary of theanatomical portion. Diffusion filtering may be performed to dilate edgepoints that correspond to the structured point cloud to mutually connectthe edge points on the structured point cloud. A mask may be created forthe anatomical portion from the diffusion filtering.

In accordance with an embodiment, the anatomical portion may correspondto a skull portion, a knee cap portion, or other anatomical portions ofa subject. The multimodal images may be received from a plurality ofmedical imaging devices. The received multimodal images may correspondto different sets of unregistered images associated with the anatomicalportion of a subject. The plurality of multimodal images may correspondto X-ray computed tomography (CT), magnetic resonance imaging (MRI),magnetic resonance angiography (MRA), fluid-attenuated inversionrecovery (FLAIR), and/or positron emission tomography (PET).

In accordance with an embodiment, volumetric edges of the anatomicalportion of the subject may be detected by use of a first set of images.The first set of images may be obtained from at least one of theplurality of medical imaging devices that captures the anatomicalportion from different points-of-view.

In accordance with an embodiment, one or more surface layers of theanatomical portion may be computed based on registration of themultimodal images. Mutual information may be computed for structuresthat overlap in the associated multimodal images, the anatomical portionof the subject. The amount of co-occurrence information may be measuredfor the overlapped structures that contain smooth gradients in thecomputed one or more surface layers to compute the mutual information.In accordance with an embodiment, the computed mutual information may beoptimized by use of a gradient descent technique, known in the art.

In accordance with an embodiment, the computed mutual information may bemodified by application of higher spatial weights around one of thecomputed one or more surface layers in comparison to other surfacelayers. The one surface layer may correspond to a skull surface. Inaccordance with an embodiment, skull structure information associatedwith the one surface layer may be identified from MRI data, based on thecreated mask.

In accordance with an embodiment, a plurality of multi-dimensionalgraphical views of the anatomical portion may be generated. Thegenerated plurality of multi-dimensional graphical views may comprise afirst set of views that further comprises the identified skull structureinformation associated with the one surface layer. The generatedplurality of multi-dimensional graphical views may further comprise asecond set of views that further comprises the identified skullstructure information, together with underlying tissue information,which corresponds to the other surface layers. In accordance with anembodiment, the generated plurality of multi-dimensional graphical viewsmay correspond to one or more perspectives of a three-dimensional (3D)view of the anatomical portion.

In accordance with an exemplary aspect of the disclosure, a structuredpoint cloud that represents edge points of a skull portion may begenerated. The structured point cloud for the skull portion may begenerated based on shrink-wrapping of an unstructured point cloud to aboundary of the skull portion. Mutual information may be computed for aplurality of structures that overlap in the multimodal images associatedwith the skull portion. The boundary of the skull portion corresponds toone of the plurality of overlapped structures. The computed mutualinformation may be computed by application of higher spatial weightsaround a skull surface layer of the skull portion in comparison to otherunderlying brain surface layers of the skull portion. The skull surfacelayer and the underlying brain surface layers of the skull portion maybe computed based on alignment of bone structure of the skull portion inthe multimodal images.

FIG. 1 is a block diagram that illustrates a network environment toprocess multimodal images, in accordance with an embodiment of thedisclosure. With reference to FIG. 1, there is shown an exemplarynetwork environment 100. The network environment 100 may include animage-processing device 102, a plurality of medical imaging devices 104,multimodal images 106, a server 108, a communication network 110, one ormore users, such as a human subject 112, and a medical assistant 114.The multimodal images 106 may include different sets of unregisteredimages 106 a to 106 e of an anatomical portion of a subject, such as thehuman subject 112. The image-processing device 102 may becommunicatively coupled to the plurality of medical imaging devices 104and the server 108, via the communication network 110.

The image-processing device 102 may comprise suitable logic, circuitry,interfaces, and/or code that may be configured to process the multimodalimages 106, obtained from the plurality of medical-imaging devices 104.In accordance with an embodiment, the image-processing device 102 may beconfigured to display a plurality of multi-dimensional, such astwo-dimensional (2D) or three-dimensional (3D), graphical views of theanatomical portion. The plurality of multi-dimensional graphical viewsof the anatomical portion, such as the skull portion, may be a result ofprocessing of the multimodal images 106. In accordance with anembodiment, such display may occur in real-time, or near real-time,while a surgical or diagnostic procedure is performed on the anatomicalregion of the subject, such as the human subject 112. In accordance withan embodiment, such display may also occur in preoperative,intraoperative, or postoperative states of the subject, as peruser-defined configuration settings. Examples of the image-processingdevice 102 may include, but are not limited to, a user terminal or anelectronic device associated with a computer-assisted surgical system ora robot-assisted surgical system, a medical device, an electronicsurgical instrument, a tablet computer, a laptop, a display device,and/or a computing device.

The plurality of medical-imaging devices 104 may correspond todiagnostic equipment used to create visual representations of internalstructures or anatomical portions of a subject, such as the humansubject 112. The visual representations from the diagnostic equipmentmay be used for clinical analysis and medical intervention. Examples ofthe plurality of medical-imaging devices 104 may include, but are notlimited to, an X-ray computed tomography (CT) scanner, a magneticresonance imaging (MRI) scanner, a magnetic resonance angiography (MRA)scanner, a fluid-attenuated inversion recovery (FLAIR) based scanner,and/or a positron emission tomography (PET) scanner.

The multimodal images 106 correspond to images and/or data obtained frommultimodality, such as the plurality of medical imaging devices 104. Forinstance, the multimodal images 106 may include the different sets ofunregistered images 106 a to 106 e of the anatomical portion, such as askull portion, of the subject. The multimodal images 106 may correspondto a first set of images 106 a or data obtained from the MRI modality.The multimodal images 106 may further correspond to a second set ofimages 106 b, obtained from the CT-based medical-imaging technique.Similarly, the multimodal images 106 may also include a third set ofimages 106 c obtained from MRA-based medical imaging technique, a fourthset of images 106 d obtained from the FLAIR-based medical imagingtechnique, and finally, a fifth set of images 106 e obtained from thePET-based medical imaging technique.

The server 108 may comprise suitable logic, circuitry, interfaces,and/or code that may be configured to receive and centrally store themultimodal images 106 and associated data obtained from the plurality ofmedical-imaging devices 104. In accordance with an embodiment, theserver 108 may be configured to provide the stored multimodal images 106to the image-processing device 102. In accordance with an embodiment,the image-processing device 102 may directly receive the multimodalimages 106 from the plurality of medical-imaging devices 104. Inaccordance with an embodiment, both the server 108 and theimage-processing device 102 may be part of a computer-assisted surgicalsystem. In accordance with an embodiment, the server 108 may beimplemented as a plurality of cloud-based resources by use of severaltechnologies that are well known to those skilled in the art. Examplesof the server 108 may include, but are not limited to, a databaseserver, a file server, an application server, a web server, and/or theircombination.

The communication network 110 may include a medium through which theimage-processing device 102, the plurality of medical-imaging devices104, and/or the server 108 may communicate with each other. Thecommunication network 110 may be a wired or wireless communicationnetwork. Examples of the communication network 110 may include, but arenot limited to, a Local Area Network (LAN), a Wireless Local AreaNetwork (WLAN), a cloud network, a Long Term Evolution (LTE) network, aplain old telephone service (POTS), a Metropolitan Area Network (MAN),and/or the Internet. Various devices in the network environment 100 maybe configured to connect to the communication network 110, in accordancewith various wired and wireless communication protocols. Examples ofsuch wired and wireless communication protocols may include, but are notlimited to, Transmission Control Protocol and Internet Protocol(TCP/IP), User Datagram Protocol (UDP), Hypertext Transfer Protocol(HTTP), File Transfer Protocol (FTP), ZigBee, EDGE, infrared (IR), IEEE802.11, 802.16, cellular communication protocols, and/or Bluetooth (BT)communication protocols.

In operation, the image-processing device 102 may be configured toreceive the multimodal images 106 from the plurality of medical-imagingdevices 104. The received multimodal images 106 may correspond to thedifferent sets of unregistered images 106 a to 106 e associated with ananatomical portion of a subject, such as the human subject 112. Inaccordance with an embodiment, the anatomical portion may be a skullportion of the human subject 112. In accordance with an embodiment, theanatomical portion may be a knee cap part, or other anatomical portionsof the human subject 112. A person with ordinary skill in the art willunderstand that the scope of the disclosure is not limited toimplementation of the disclosed system and method to process themultimodal images 106 of the anatomical portion of the human subject112, as shown. In accordance with an embodiment, the multimodal images106 of the anatomical portion of an animal subject may be processed asrequired, without deviation from the scope of the disclosure.

The multimodal images 106 may exhibit structural, resolution, and/orclinical usage differences, in the different sets of unregistered images106 a to 106 e. For example, structural differences may be observed whena comparison is performed among the first set of images 106 a, thesecond set of images 106 b, and the third set of images 106 c. The firstset of images 106 a (obtained from the MRI), may provide tissue and bonestructure information for an anatomical portion, such as the skullportion. The second set of images 106 b (obtained from the CT-basedmedical-imaging technique), may provide bone structure information ofthe anatomical portion rather than tissue information. The third set ofimages 106 c may also comprise vessel information of the same anatomicalportion, such as brain surface structures of the same subject.

In another example, the resolution of the fifth set of images 106 e(obtained from PET-based medical-imaging techniques), may be low ascompared to other sets of images, such as the fourth set of images 106 d(obtained from the FLAIR). The first set of images 106 a (obtained fromthe MRI), and/or the second set of images 106 b (obtained from theCT-based medical-imaging technique), may have higher resolution ascompared to the resolution of the fifth set of images 106 e. Thus,resolution differences may also be observed in the multimodal images106. Further, the first set of images 106 a (obtained from the MRI), maybe used for the purposes of planning a surgery. On the contrary, thefourth set of images 106 d (obtained from the FLAIR) and the fifth setof images 106 e (obtained from PET) are usually used for diagnosticpurposes. Thus, clinical usage differences may also be observed in themultimodal images 106.

In accordance with an embodiment, to register the multimodal images 106from different modalities, such as the CT and MRI, the multimodal images106 must include overlapped content. The structural, resolution, and/orclinical usage differences, in the different sets of unregistered images106 a to 106 e of the multimodal images 106 may make registration adifficult task. In accordance with an embodiment, the image-processingdevice 102 may be configured to locate common information content acrossthe multimodal images 106. At least a reference point, which isinvariable for the same subject in two or more sets of images obtainedfrom different modalities, may be identified and utilized forregistration of the multimodal images 106. For example, forregistration, the image-processing device 102 may be configured to alignthe bone structure of a skull portion in the multimodal images 106(which may comprise data obtained from the CT scan and the MRI of thesame subject). The common information content may be identified andisolated across different image modalities as the spatial alignment ofthe bone structure of the skull portion, which is invariable for thesame subject. Focus on a specific structure, such as the bone structureof the skull portion of the anatomy, may allow non-overlapping segmentsof the image content to be excluded, which increases the accuracy of theregistration.

In accordance with an embodiment, the image-processing device 102 may beconfigured to detect volumetric edges of the anatomical portion of thesubject, such as the human subject 112. The volumetric edges of theanatomical portion may be detected by use of data obtained from at leastone of the plurality of medical-imaging devices 104, which captures theanatomical portion from different points-of-view. In accordance with anembodiment, the data may be a first set of images 106 a of theanatomical portion, such as the skull portion, obtained from the MRI.

The image-processing device 102 may be configured to register themultimodal images, such as the different sets of images 106 a to 106 e,based on the identified reference point. In accordance with anembodiment, the image-processing device 102 may be configured to computeone or more surface layers of the anatomical portion based onregistration of the multimodal images. For example, the image-processingdevice 102 may compute the skull surface layer and underlying brainsurface layers of the skull portion, based on the alignment of the bonestructure of the skull portion in the multimodal images 106.

In accordance with an embodiment, the image-processing device 102 may beconfigured to compute mutual information for overlapping structures inthe multimodal images 106, which may be associated with the anatomicalportion of the subject. Non-overlapped structures may be considered asoutliers. An amount of co-occurrence information may be measured for theoverlapped structures with smooth gradients in the computed one or moresurface layers. The result may be used to compute the mutualinformation. The mutual information for the overlapping structures inthe multimodal images 106 may be computed by use of the followingmathematical expressions:

$\begin{matrix}{{I\left( {A,B} \right)} = {\sum\limits_{a}{\sum\limits_{b}{{P_{AB}\left( {a,b} \right)}\log \frac{P_{AB}\left( {a,b} \right)}{{P_{A}(a)}{P_{B}(b)}}}}}} & (1) \\{{I\left( {A,B} \right)} = {{H(A)} + {H(B)} - {H\left( {A,B} \right)}}} & (2) \\{{H(x)} = {- {\sum\limits_{i}{{p\left( x_{i} \right)}\log \; {p\left( x_{i} \right)}}}}} & (3)\end{matrix}$

In accordance with the expression (1), “I(A, B)” corresponds to themutual information of two discrete random variables A and B associatedwith the multimodal images 106. “P_(AB) (a, b)” may be the jointprobability distribution function of random variables A and B.“P_(A)(a)” may be the marginal probability distribution function of therandom variable A and “P_(B)(b)” may be the marginal probabilitydistribution function of the other random variable B. In accordance withexpression (2), “H(A)” and “H(B)” corresponds to marginal entropies ofthe respective discrete random variables A and B of the associatedmultimodal images 106, and “H(A,B)” corresponds to joint entropy of thediscrete random variables A and B. In accordance with the expression(3), Shannon entropy, “H(x)” corresponds to entropy of the discreterandom variable, “x”, with possible values {x₁, x₂, . . . , x_(n)} for afinite sample associated with a certain number of multimodal images 106,where “p(x_(i))” is the probability of information or character number,“i”, in the discrete random variable “x”. The Shannon entropy maymeasure the uncertainty in the discrete random variable “x”.

In accordance with an embodiment, the image-processing device 102 may beconfigured to modify the computed mutual information. The computedmutual information may be modified by application of higher spatialweights around one surface layer, such as skull surface layer, of thecomputed one or more surface layers in comparison to other surfacelayers.

In accordance with an embodiment, the image-processing device 102 may beconfigured to generate a structured point cloud (such as a skull pointcloud), which represents edge points (such as edge points on the skullsurface) of the anatomical portion. The structured point cloud may begenerated based on shrink-wrapping of an unstructured point cloud to aboundary of the anatomical portion (described in FIG. 3C in an example).In accordance with an embodiment, the boundary may correspond to thedetected volumetric edges of the anatomical portion, such as the skullportion, of the subject.

In accordance with an embodiment, the image-processing device 102 may beconfigured to perform diffusion filtering to dilate edge points of thestructured point cloud (further described in FIG. 3D). The dilation ofthe edge points of the structured point cloud may be performed tomutually connect the edge points in the structured point cloud. Theimage-processing device 102 may be configured to create a mask for theanatomical portion based on the diffusion filtering. The mask may be acontinuous surface that may make possible optimum usage of various data,such as MRI data of the anatomical portion, to achieve accurate fusionof information obtained from the multimodality sources. The creation ofthe mask from the diffusion filtering may be an efficient process. Thecreation of the mask from the diffusion filtering may be lesscomputationally intensive operation as compared to creation of apolygonal or triangular mesh structure from the structured point cloudto obtain a continuous surface. Further, the polygonal or triangularmesh structure may require higher storage space than the created mask.

In accordance with an embodiment, the image-processing device 102 may beconfigured to further identify skull structure information associatedwith the one surface layer (such as the skull surface layer), from MRIdata, based on the created mask. The image-processing device 102 may beconfigured to apply the identified skull structure information from MRIdata and/or the other computed and modified mutual information on and/orwithin the created mask to generate enhanced visual representations.

The image-processing device 102 may be configured to generate aplurality of multi-dimensional graphical views, such as a 3D view, ofthe anatomical portion as required, which may be used to plan or performa surgery on the anatomical portion or for enhanced diagnosis of anailment in the anatomical portion. Based on the operative state (such aspreoperative, intraoperative, or postoperative), and/or received userinput, different interactive graphical views of the anatomical portionmay be generated. In accordance with an embodiment, user-configurationsmay be pre-defined or changed in real time or near real time, by themedical assistant 114, based on instructions received from a registeredmedical practitioner. The user configurations may be used to generatedifferent pluralities of multi-dimensional graphical views of theanatomical portion as required. Thus, the generated plurality ofmulti-dimensional graphical views may be user-controlled and interactiveand may be changed and visualized, as medically required.

In accordance with an embodiment, the generated plurality ofmulti-dimensional graphical views may provide enhanced views of theanatomical portion from one or more perspectives. The generatedplurality of multi-dimensional graphical views may comprise a first setof views that includes the identified skull structure informationassociated with the one surface layer (such as the skull surface layer).The generated plurality of multi-dimensional graphical views may alsoinclude a second set of views that includes the identified skullstructure information together with underlying tissue information, whichcorrespond to the other surface layers, such as brain surface structureswhen the anatomical portion is the skull portion. The brain surfacestructures may be gray matter, white matter, ventricular structures,vessel structure, the thalamus, and/or other tissue structures.

FIG. 2 illustrates a block diagram of an exemplary image-processingdevice to process multimodal images, in accordance with an embodiment ofthe disclosure. FIG. 2 is explained in conjunction with elements fromFIG. 1. With reference to FIG. 2, there is shown the image-processingdevice 102. The image-processing device 102 may comprise one or moreprocessors, such as a processor 202, a memory 204, one or moreinput/output (I/O) devices, such as an I/O device 206, and a networkinterface 208. The I/O device 206 may include a display 210.

The processor 202 may be communicatively coupled to the I/O device 206the memory 204, and the network interface 208. The network interface 208may communicate with one or more servers, such as the server 108, and/orthe plurality of medical-imaging devices 104, via the communicationnetwork 110 under the control of the processor 202.

The processor 202 may comprise suitable logic, circuitry, interfaces,and/or code that may be configured to execute a set of instructionsstored in the memory 204. The processor 202 may be further configured toprocess the multimodal images 106 received from the plurality ofmedical-imaging devices 104 or a central device, such as the server 108.The processor 202 may be implemented based on a number of processortechnologies known in the art. Examples of the processor 202 may be anX86-based processor, X86-64-based processor, a Reduced Instruction SetComputing (RISC) processor, an Application-Specific Integrated Circuit(ASIC) processor, a Complex Instruction Set Computing (CISC) processor,a central processing unit (CPU), an Explicitly Parallel InstructionComputing (EPIC) processor, a Very Long Instruction Word (VLIW)processor, and/or other processors or circuits.

The memory 204 may comprise suitable logic, circuitry, and/or interfacesthat may be configured to store a machine code and/or a set ofinstructions executable by the processor 202. The memory 204 may beconfigured to store information from one or more user profilesassociated with physiological data or medical history of the subject(such as the human subject 112). The memory 204 may be furtherconfigured to store user-defined configuration settings to generate theplurality of multi-dimensional graphical views of the anatomicalportion. The plurality of multi-dimensional graphical views of theanatomical portion may be displayed on a user interface (UI) rendered onthe display 210. The UI may be a 3D viewer or a 2D viewer. The memory204 may be further configured to store operating systems and associatedapplications. Examples of implementation of the memory 204 may include,but are not limited to, Random Access Memory (RAM), Read Only Memory(ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM),Hard Disk Drive (HDD), a Solid-State Drive (SSD), a CPU cache, and/or aSecure Digital (SD) card.

The I/O device 206 may comprise suitable logic, circuitry, interfaces,and/or code that may be configured to receive an input from and providean output to a user, such as the medical assistant 114. The I/O device206 may include various input and output devices that may be configuredto facilitate communication between the image-processing device 102 andthe user (such as the medical assistant 114). Examples of the inputdevices may include, but are not limited to, a touch screen, a camera, akeyboard, a mouse, a joystick, a microphone, a motion sensor, a lightsensor, and/or a docking station. Examples of the output devices mayinclude, but are not limited to, the display 210, a projector screen,and/or a speaker.

The network interface 208 may comprise suitable logic, circuitry,interfaces, and/or code that may be configured to communicate with oneor more servers, such as the server 108, and/or the plurality ofmedical-imaging devices 104, via the communication network 110 (as shownin FIG. 1). The network interface 208 may implement known technologiesto support wired or wireless communication of the image-processingdevice 102 with the communication network 110. The network interface 208may include, but is not limited to, an antenna, a radio frequency (RF)transceiver, one or more amplifiers, a tuner, one or more oscillators, adigital signal processor, a coder-decoder (CODEC) chipset, a subscriberidentity module (SIM) card, and/or a local buffer. The network interface208 may communicate via wired or wireless communication with thecommunication network 110. The wireless communication may use one ormore of the communication standards, protocols and technologies, such asGlobal System for Mobile Communications (GSM), Enhanced Data GSMEnvironment (EDGE), wideband code division multiple access (W-CDMA),code division multiple access (CDMA), time division multiple access(TDMA), Bluetooth, LTE, Wireless Fidelity (Wi-Fi) (such as IEEE 802.11a,IEEE 802.11b, IEEE 802.11g and/or IEEE 802.11n), voice over InternetProtocol (VoIP), Wi-MAX, a protocol for email, instant messaging, and/orShort Message Service (SMS).

The display 210 may be realized through several known technologies, suchas Cathode Ray Tube (CRT) based display, Liquid Crystal Display (LCD),Light Emitting Diode (LED) based display, Organic LED displaytechnology, Retina display technology, and/or the like. In accordancewith an embodiment, the display 210 may be capable of receiving inputfrom the user (such as the medical assistant 114). In such a scenario,the display 210 may be a touch screen that enables the user to providethe input. The touch screen may correspond to at least one of aresistive touch screen, a capacitive touch screen, or a thermal touchscreen. In accordance with an embodiment, the display 210 may receivethe input through a virtual keypad, a stylus, a gesture-based input,and/or a touch-based input. In such a case, the input device may beintegrated within the display 210. In accordance with an embodiment, theimage-processing device 102 may include a secondary input device apartfrom the display 210 that may be a touch screen based display.

In operation, the processor 202 may be configured to receive themultimodal images 106 from the plurality of medical-imaging devices 104,by use of the network interface 208. The received multimodal images 106may correspond to different sets of unregistered images 106 a to 106 e,associated with the anatomical portion of the subject, such as the humansubject 112. The operations performed by the processor 202 have beenfurther described in the FIGS. 3A to 3F, by an example of the skullportion of the human subject 112, as the anatomical portion.Notwithstanding, the anatomical portion may also be a knee cap part, orother anatomical portions of the subject of which the multimodal images106 may be obtained from the plurality of the medical-imaging devices104, without limiting the scope of the disclosure.

FIGS. 3A to 3F, collectively, illustrate an exemplary scenario forimplementation of the disclosed system and method to process multimodalimages, in accordance with an embodiment of the disclosure. FIG. 3Aillustrates receipt of multimodal images for a skull portion of asubject in the exemplary scenario for implementation of the system andmethod, in accordance with an embodiment of the disclosure. FIG. 3A isexplained in conjunction with FIG. 1 and FIG. 2. With reference to FIG.3A, there are shown medical images 302 a to 302 e of the same skullportion of the same subject received from the plurality ofmedical-imaging devices 104, such as an MRI scanner 304 a, a CT scanner304 b, an MRA scanner 304 c, a FLAIR scanner 304 d, and a PET scanner304 e, respectively. There is further shown a bone structure 306 of theskull portion of the human subject 112, common to the medical images 302a to 302 e.

In accordance with the exemplary scenario, the medical images 302 a to302 e of the skull portion may correspond to the multimodal images 106.The medical image 302 a may be an output of the MRI scanner 304 a of theskull portion of the human subject 112. A number of medical images maybe obtained from the MRI scanner 304 a from different points-of-viewthat may be referred to as a first set of medical images. The first setof medical images may correspond to first set of images 106 a (FIG. 1).As the medical image 302 a represents a view of the skull portion fromone point-of-view, the first set of medical images may represent acaptured view of the skull portion from different points-of-view.Similarly, the medical image 302 b may be obtained from the CT scanner304 b. The medical image 302 c may be obtained from the MRA scanner 304c. The medical image 302 d may be obtained from the FLAIR scanner 304 d,and finally the medical image 302 e may be obtained from the PET scanner304 e. The output, such as the medical images 302 a to 302 e, receivedfrom multimodal sources, as described above, may be stored at a centraldevice, such as the server 108. In such a case, the processor 202 mayreceive the medical images 302 a to 302 e from the server 108. Inaccordance with an embodiment, the medical images 302 a to 302 e may bestored at the memory 204.

In accordance with an embodiment, the processor 202 may be configured toprocess the received medical images 302 a to 302 e. The processor 202may be configured to align the bone structure 306 of the same skullportion of the same human subject 112 for the registration of theunregistered medical images 302 a to 302 e. As the bone structure 306 isinvariable for the same human subject 112, it may be used as a referencepoint to preliminarily register the medical images 302 a to 302 e. Theprocessor 202 may be configured to identify and isolate the bonestructure 306 of the skull portion across the received medical images302 a to 302 e. This makes possible exclusion of the non-overlapped partor outliers of the bone structure 306 in the medical images 302 a to 302e.

In accordance with an embodiment, the processor 202 may be configured todetect volumetric edges of the skull portion of the human subject 112,by use of the first set of medical images captured by the MRI scanner304 a from different points-of-view (also referred to as MRI slices). Inother words, different medical images or data captured from variousperspectives for the same skull portion from a single modality, such asthe MRI scanner 304 a, may also be used to detect the volumetric edgesof the skull portion based on the alignment of the bone structure 306 asthe reference point. In accordance with an embodiment, the volumetricedges of the skull portion may represent boundary of the skull portionin a 3D space.

FIG. 3B illustrates surface layers of the skull portion computed basedon the registration of the multimodal images in the exemplary scenariofor implementation of the system and method, in accordance with anembodiment of the disclosure. FIG. 3B is explained in conjunction withFIGS. 1, 2, and 3A. With reference to FIG. 3B, there is shown a skullsurface layer 308 and a brain surface layer 310, computed based on thealignment of the bone structure 306 of the skull portion in the medicalimages 302 a to 302 e. The skull surface layer 308 may represent theskull surface of the skull portion. The brain surface layer 310 mayinclude one or more brain surface structures, such as a cerebrum surfacestructure, cerebellum surface structure, vessel structures, other braintissue information, or brain ventricular structures.

In accordance with an embodiment, the processor 202 may be configured tocompute one or more surface layers of the skull portion based on theregistration. The processor 202 may compute the skull surface layer 308,based on the alignment of the bone structure 306 of the skull portion inthe medical images 302 a to 302 e (such as the multimodal images). Inaccordance with an embodiment, the processor 202 may compute both theskull surface layer 308 and the underlying brain surface layer 310 ofthe skull portion, based on the alignment of the bone structure of theskull portion in the medical images 302 a to 302 e. In accordance withan embodiment, the first set of medical images, such as MRI data, ordata obtained from one or two modality instead of all of the pluralityof medical-imaging devices 104, may be used as required for computationof the one or more surface layers of the skull portion.

In accordance with an embodiment, the processor 202 may be configured tocompute mutual information for structures that overlap in the medicalimages 302 a to 302 e, associated with the skull portion of the humansubject 112. The mutual information may be computed, in accordance withthe mathematical expressions (1), (2), and/or (3), as described inFIG. 1. The amount of co-occurrence information may be measured for theoverlapped structures with smooth gradients in the computed one or moresurface layers (such as the skull surface layer 308 and the brainsurface layer 310), to compute the mutual information.

In accordance with an embodiment, the processor 202 may be configured tomodify the computed mutual information by application of higher spatialweights around one surface layer, such as a skull surface, of thecomputed one or more surface layers in comparison to other surfacelayers. In other words, the reliable structures, such as the skullsurface layer 308, may be weighted more than the comparatively lessreliable structures, such as vessel structures of the brain surfacelayer 310. The application of higher spatial weights around the reliablestructures increases the accuracy for computation of the mutualinformation across the medical images 302 a to 302 e.

FIG. 3C illustrates creation of a mask for a skull portion in theexemplary scenario for implementation of the system and method, inaccordance with an embodiment of the disclosure. FIG. 3C is explained inconjunction with FIGS. 1, 2, 3A, and 3B. With reference to FIG. 3C,there is shown a skull point cloud 312 and a mask 314. The skull pointcloud 312 corresponds to the structured point cloud of the anatomicalportion. In accordance with an embodiment, the skull point cloud 312 mayrepresent edge points of the detected volumetric edges of the skullportion, such as the boundary of skull surface, as point cloud. The mask314 may be a continuous structure generated from the skull point cloud312. The mask 314 may represent the skull surface layer 308 of the skullportion. The mask 314 may also be representative of a current skullstate, such as an open state of skull during a surgery or a closed stateof skull in the preoperative or postoperative phase of a surgery.

In accordance with an embodiment, the processor 202 may be configured togenerate the structured point cloud, such as the skull point cloud 312,which represents edge points on the skull surface. The structured pointcloud may be generated based on shrink-wrapping of an unstructured pointcloud to a boundary of the skull portion. In accordance with anembodiment, the boundary of the skull portion may correspond to thedetected volumetric edges of the skull portion of the human subject 112.

In accordance with an embodiment, the unstructured point cloud maycorrespond to the point cloud obtained from 3D scanners or other pointcloud generators known in the art, such as a laser range scanner (LRS).In accordance with an embodiment, the unstructured point cloud maycorrespond to the point cloud obtained by use of stereoscopic imagesfrom stereo vision, or based on computer vision that may capture theskull portion from a plurality of points-of-view. In accordance with anembodiment, the unstructured point cloud may correspond to point cloudcreated from the 2D medical images 302 a to 302 e (multimodal images ofthe skull portion).

In accordance with an embodiment, the processor 202 may be configured toperform diffusion filtering to dilate edge points of the skull pointcloud 312 to mutually connect the edge points in the skull point cloud312. The processor 202 may be configured to create the mask 314 for theskull portion based on the diffusion filtering.

FIG. 3D illustrates diffusion filtering of edge points of an exemplaryskull point cloud in the exemplary scenario for implementation of thesystem and method, in accordance with an embodiment of the disclosure.FIG. 3D is explained in conjunction with FIGS. 1, 2, 3A, 3B, and 3C.With reference to FIG. 3D, there is shown a skull point cloud 312, apoint center 316, and a graph 318.

The point center 316 corresponds to a centroid of a point of the skullpoint cloud 312, as shown. The graph 318 corresponds to a diffusionfilter that represents the filter strength on the Y-axis and distancefrom the point center 316 on the X-axis, as shown. The diffusion filterdomain may be a 3D sphere with the same depicted profile in all threedirections (such as X-, Y-, and Z-axis directions), as illustrated bythe arrows.

In accordance with an embodiment, the processor 202 may be configured tocontrol the thickness of the skull surface layer 308. Based on thecalculation of total time taken for the decay of the diffusion filter,and subsequent configuration of the total time, the thickness of theskull surface layer 308 may be controlled. In other words, the skullthickness may be controlled based on how fast the diffusion filterdecays. In accordance with an embodiment, the diffusion filter may becentered at each point of the skull point cloud 312 and convolved withthe skull point cloud 312. Accordingly, each point of the skull pointcloud 312 may dilate to mutually connect with each other. Such dilationand mutual connection may occur in all the three directions, such as inthe X-, Y-, and Z-direction, to create the mask 314 of the skullportion.

In accordance with an embodiment, the processor 202 may be configured toidentify skull structure information associated with the skull surfacelayer 308, from the MRI data based on the created mask 314. Inaccordance with an embodiment, the processor 202 may be configured toidentify tissue information of the brain surface layer 310, based on thecomputed mutual information, in accordance with the mathematicalexpressions (1), (2), and/or (3), as described in FIG. 1.

FIG. 3E illustrates generation of an enhanced view of the skull portionin the exemplary scenario for implementation of the system and method,in accordance with an embodiment of the disclosure. FIG. 3E is explainedin conjunction with FIGS. 1, 2, 3A, 3B, 3C, and 3D. With reference toFIG. 3E, there is shown an enhanced view 320 of the skull portion.

The processor 202 may be configured to utilize the MRI data of the skullportion and the created mask 314, to generate the enhanced view 320 ofthe skull portion. In accordance with an embodiment, the MRI data of theskull portion may be applied on the created mask 314 for the generationof the enhanced view 320 of the skull portion. The MRI data may be theidentified skull structure information associated with the skullportion. In accordance with an embodiment, the modified mutualinformation associated with the skull surface layer 308 and othercomputed mutual information associated with the skull portion may befurther utilized and applied on the created mask 314, to generate theenhanced view 320 of the skull portion.

FIG. 3F illustrates different views of a skull portion in the exemplaryscenario for implementation of the system and method, in accordance withan embodiment of the disclosure. FIG. 3F is explained in conjunctionwith FIGS. 1, 2, 3A, 3B, 3C, 3D, and 3E. With reference to FIG. 3F,there is shown a first top view 322 of the skull portion in thepreoperative state and a second top view 324 of the skull portion in theintraoperative stage. There is further shown a first bottom view 326 ofthe skull point cloud 312, a second bottom view 328 of the skull portionin the intraoperative state, and a third bottom view 330 of the skullportion in the preoperative state together with brain tissue information332.

The processor 202 may be configured to generate a plurality ofmulti-dimensional graphical views, such as the views 322 to 332, of theskull portion. The generated plurality of multi-dimensional graphicalviews may provide enhanced views of the skull portion from one or moreperspectives. The generated plurality of multi-dimensional graphicalviews may comprise a first set of views that includes the identifiedskull structure information associated with the skull surface layer 308.The first top view 322, the second top view 324, the first bottom view326, and the second bottom view 328, all correspond to the first set ofviews that includes the identified skull structure informationassociated with the skull surface layer 308.

The generated plurality of multi-dimensional graphical views may alsoinclude a second set of views that includes the identified skullstructure information, together with underlying tissue information thatcorresponds to the other surface layers, such as brain surfacestructures of the brain surface layer 310. The third bottom view 330 ofthe skull portion in the preoperative state, together with brain tissueinformation 332, corresponds to the second set of views that includesthe identified skull structure information together with underlyingtissue information.

The processor 202 may be configured to control display of the generatedplurality of multi-dimensional graphical views, such as a 2D view or a3D view, of the skull portion on the UI. The displayed plurality ofmulti-dimensional graphical views may be interactive anduser-controlled, based on input received from the I/O device 206. Theuser input may be received by use of the UI rendered on the display 210,of the image-processing device 102. The display of the plurality ofmulti-dimensional graphical views may be changed and updated in responseto the received user input, such as input provided by the medicalassistant 114. Such enhanced visualization of the multi-dimensionalgraphical views of the skull portion on the UI may be utilized by users,such as a physician, for diagnostic purposes and/or for provision ofreal-time or near real-time assistance in a surgery.

FIG. 4 illustrates a flow chart for implementation of an exemplarymethod to process multimodal images, in accordance with an embodiment ofthe disclosure. With reference to FIG. 4, there is shown a flow chart400. The flow chart 400 is described in conjunction with FIGS. 1, 2, and3A to 3F. The method, in accordance with the flowchart 400, may beimplemented in the image-processing device 102. The method starts atstep 402 and proceeds to step 404.

At step 404, multimodal images 106 from the plurality of medical-imagingdevices 104 may be received. The received multimodal images 106 maycorrespond to different sets of unregistered images 106 a to 106 e,associated with an anatomical portion of a subject, such as the humansubject 112. The anatomical portion may be a skull portion, a knee cappart, or other anatomical portions of the subject. The subject may bethe human subject 112 or an animal subject (not shown). At step 406,volumetric edges of the anatomical portion of the subject may bedetected by use of a first set of images. The first set of images fromthe different sets of unregistered images may be obtained from at leastone of the plurality of medical-imaging devices 104, such as the MRIscanner, which captures the anatomical portion from differentpoints-of-view.

At step 408, the multimodal images 106 may be registered based on areference point. For example, for registration, the image-processingdevice 102 may be configured to align the bone structure 306 of theskull portion in the multimodal images 106, such as data obtained fromthe CT scan and the MRI. At step 410, one or more surface layers of theanatomical portion may be computed based on registration of themultimodal images 106, such as the medical images 302 a to 302 e. Forexample, the skull surface layer 308 and underlying brain surface layer310 of the skull portion may be computed based on the alignment of thebone structure 306 of the skull portion in the medical images 302 a to302 e.

At step 412, mutual information may be computed for structures thatoverlap in the multimodal images 106, associated with the anatomicalportion of the subject (such as the human subject 112). The mutualinformation may be computed, in accordance with the mathematicalexpressions (1), (2), and/or (3), as described in FIG. 1. The amount ofco-occurrence information may be measured for the overlapped structureswith smooth gradients in the computed one or more surface layers tocompute the mutual information. At step 414, the computed mutualinformation may be modified by an application of higher spatial weightsaround one surface layer, such as skull surface layer 308, of thecomputed one or more surface layers in comparison to other surfacelayers, such as the brain surface layer 310.

At step 416, a structured point cloud, such as the skull point cloud 312(which represents edge points, such as edge points on the skullsurface), of the anatomical portion, may be generated. The structuredpoint cloud may be generated based on shrink-wrapping of an unstructuredpoint cloud to a boundary of the anatomical portion. At step 418,diffusion filtering may be performed to dilate edge points of thestructured point cloud to mutually connect the edge points on thestructured point cloud.

At step 420, a mask, such as the mask 314, may be created for theanatomical portion based on the diffusion filtering. At step 422, skullstructure information associated with the one surface layer, such as theskull surface layer 308, may be identified from MRI data, based on thecreated mask.

At step 424, skull structure information and/or modified and computedmutual information may be applied on the created mask. At step 426, aplurality of multi-dimensional graphical views, such as a 3D view, ofthe anatomical portion may be generated. The generated plurality ofmulti-dimensional graphical views may provide enhanced views of theanatomical portion from one or more perspectives. The generatedplurality of multi-dimensional graphical views may comprise a first setof views that includes the identified skull structure informationassociated with the one surface layer, such as the skull surface. Thegenerated plurality of multi-dimensional graphical views may alsoinclude a second set of views that includes the identified skullstructure information, together with underlying tissue information thatcorresponds to the other surface layers, such as brain surfacestructures. Examples of the generated plurality of multi-dimensionalgraphical views of the skull portion has been shown and described inFIG. 3F. Control passes to end step 428.

In accordance with an embodiment of the disclosure, the system toprocess multimodal images may comprise the image-processing device 102(FIG. 1). The image-processing device 102 may comprise one or morecircuits, such as the processor 202 (FIG. 2). The processor 202 may beconfigured to generate a structured point cloud that represents edgepoints of an anatomical portion based on shrink-wrapping of anunstructured point cloud to a boundary of the anatomical portion. Theprocessor 202 may be further configured to perform diffusion filteringto dilate edge points that corresponds to the structured point cloud tomutually connect the edge points on the structured point cloud. Theprocessor 202 may be further configured to create a mask for theanatomical portion based on the diffusion filtering.

Various embodiments of the disclosure may provide a non-transitorycomputer readable medium and/or storage medium, and/or a non-transitorymachine readable medium and/or storage medium with a machine code storedthereon, and/or a set of instructions executable by a machine and/or acomputer to process multimodal images. The set of instructions in theimage-processing device 102 may cause the machine and/or computer toperform the steps that comprise generation of a structured point cloudthat represents edge points of an anatomical portion. The structuredpoint cloud may be generated based on shrink-wrapping of an unstructuredpoint cloud to a boundary of the anatomical portion. Diffusion filteringmay be performed to dilate edge points that correspond to the structuredpoint cloud to mutually connect the edge points on the structured pointcloud. A mask may be created for the anatomical portion based on thediffusion filtering.

The present disclosure may be realized in hardware, or a combination ofhardware and software. The present disclosure may be realized in acentralized fashion, in at least one computer system, or in adistributed fashion, where different elements may be spread acrossseveral interconnected computer systems. A computer system or otherapparatus adapted to carry out the methods described herein may besuited. A combination of hardware and software may be a general-purposecomputer system with a computer program that, when loaded and executed,may control the computer system such that it carries out the methodsdescribed herein. The present disclosure may be realized in hardwarethat comprises a portion of an integrated circuit that also performsother functions.

The present disclosure may also be embedded in a computer programproduct, which comprises all the features that enable the implementationof the methods described herein, and which when loaded in a computersystem is able to carry out these methods. Computer program, in thepresent context, means any expression, in any language, code ornotation, of a set of instructions intended to cause a system that hasan information processing capability to perform a particular functioneither directly, or after either or both of the following: a) conversionto another language, code or notation; b) reproduction in a differentmaterial form.

While the present disclosure has been described with reference tocertain embodiments, it will be understood by those skilled in the artthat various changes may be made and equivalents may be substitutedwithout departure from the scope of the present disclosure. In addition,many modifications may be made to adapt a particular situation ormaterial to the teachings of the present disclosure without departurefrom its scope. Therefore, it is intended that the present disclosurenot be limited to the particular embodiment disclosed, but that thepresent disclosure will include all embodiments that falls within thescope of the appended claims.

What is claimed is:
 1. A system for processing multimodal images, saidsystem comprising: one or more circuits in an image-processing deviceconfigured to: generate a structured point cloud that represents edgepoints of an anatomical portion based on shrink-wrapping of anunstructured point cloud to a boundary of said anatomical portion;perform diffusion filtering to dilate edge points corresponding to saidstructured point cloud to mutually connect said edge points on saidstructured point cloud; and create a mask for said anatomical portionbased on said diffusion filtering.
 2. The system according to claim 1,wherein said anatomical portion corresponds to one of: a skull portion,a knee cap part, or other anatomical portions of a subject.
 3. Thesystem according to claim 1, wherein said one or more circuits arefurther configured to receive said multimodal images from a plurality ofmedical-imaging devices, wherein said received multimodal imagescorrespond to different sets of unregistered images associated with saidanatomical portion of a subject, and wherein said plurality ofmultimodal images corresponds to two or more of: X-ray computedtomography (CT), magnetic resonance imaging (MRI), magnetic resonanceangiography (MRA), fluid-attenuated inversion recovery (FLAIR), and/orpositron emission tomography (PET).
 4. The system according to claim 3,wherein said one or more circuits are further configured to detectvolumetric edges of said anatomical portion of a subject using a firstset of images, wherein said first set of images are obtained from atleast one of said plurality of medical-imaging devices that capturessaid anatomical portion from different points-of-view.
 5. The systemaccording to claim 1, wherein said one or more circuits are furtherconfigured to compute one or more surface layers of said anatomicalportion based on registration of said multimodal images.
 6. The systemaccording to claim 5, wherein said one or more circuits are furtherconfigured to compute mutual information for overlapping structures insaid multimodal images associated with said anatomical portion of asubject, wherein an amount of co-occurrence information is measured foroverlapped structures with smooth gradients in said computed said one ormore surface layers to compute said mutual information.
 7. The systemaccording to claim 6, wherein said one or more circuits are furtherconfigured to modify said computed mutual information by applying higherspatial weights around one of said computed said one or more surfacelayers in comparison to other surface layers, wherein said one surfacelayer corresponds to a skull surface.
 8. The system according to claim7, wherein said one or more circuits are further configured to identifyskull structure information associated with said one surface layer frommagnetic resonance imaging (MRI) data based on said created mask.
 9. Thesystem according to claim 8, wherein said one or more circuits arefurther configured to generate a plurality of multi-dimensionalgraphical views of the anatomical portion, wherein said generatedplurality of multi-dimensional graphical views comprises one or more of:a first set of views comprising said identified skull structureinformation associated with said one surface layer and a second set ofviews comprising said identified skull structure information togetherwith underlying tissue information and/or vessel information thatcorresponds to said other surface layers.
 10. The system according toclaim 9, wherein said generated plurality of multi-dimensional graphicalviews corresponds to a three dimensional view of said anatomical portionfrom one or more perspectives.
 11. A system for processing multimodalimages, said system comprising: one or more circuits in animage-processing device configured to: generate a structured point cloudthat represents edge points of a skull portion based on shrink-wrappingof an unstructured point cloud to a boundary of said skull portion;compute mutual information for a plurality of overlapping structures inmultimodal images associated with said skull portion, wherein saidboundary of said skull portion corresponds to one of said plurality ofoverlapping structures; and modify said computed mutual information byapplying higher spatial weights around a skull surface layer of saidskull portion in comparison to other underlying brain surface layers ofsaid skull portion.
 12. The system according to claim 11, wherein saidone or more circuits are further configured to compute said skullsurface layer and underlying brain surface layers of said skull portionbased on alignment of bone structure of said skull portion in saidmultimodal images.
 13. A method for processing multimodal images, saidmethod comprising: generating, by one or more circuits in animage-processing device, a structured point cloud that represents edgepoints of an anatomical portion based on shrink-wrapping of anunstructured point cloud to a boundary of said anatomical portion;performing, by said one or more circuits, diffusion filtering to dilateedge points corresponding to said structured point cloud to mutuallyconnect said edge points on said structured point cloud; and creating,by said one or more circuits, a mask for said anatomical portion basedon said diffusion filtering.
 14. The method according to claim 13,wherein said anatomical portion corresponds to one of: skull, knee cap,or other anatomical portions of a subject.
 15. The method according toclaim 13, further comprising receiving, by said one or more circuits,said multimodal images from a plurality of medical-imaging devices,wherein said received multimodal images correspond to different sets ofunregistered images associated with said anatomical portion of asubject, and wherein said plurality of multimodal images corresponds totwo or more of: X-ray computed tomography (CT), magnetic resonanceimaging (MRI), magnetic resonance angiography (MRA), fluid-attenuatedinversion recovery (FLAIR), and/or positron emission tomography (PET).16. The method according to claim 15, further comprising detecting, bysaid one or more circuits, volumetric edges of said anatomical portionof a subject using a first set of images, wherein said first set ofimages are obtained from at least one of said plurality ofmedical-imaging devices that captures said anatomical portion fromdifferent points-of-view.
 17. The method according to claim 13, furthercomprising computing, by said one or more circuits, one or more surfacelayers of said anatomical portion based on registration of saidmultimodal images.
 18. The method according to claim 17, furthercomprising computing, by said one or more circuits, mutual informationfor overlapping structures in received said multimodal images associatedwith said anatomical portion of a subject, wherein an amount ofco-occurrence information is measured for overlapped structures withsmooth gradients in said computed said one or more surface layers tocompute said mutual information.
 19. The method according to claim 18,further comprising modifying, by said one or more circuits, saidcomputed mutual information by applying higher spatial weights aroundone of said computed said one or more surface layers in comparison toother surface layers, wherein said one surface layer corresponds to askull surface.
 20. The method according to claim 19, further comprisingidentifying, by said one or more circuits, skull structure informationassociated with said one surface layer from MRI data based on saidcreated mask.
 21. The method according to claim 20, further comprisinggenerating, by said one or more circuits, a plurality ofmulti-dimensional graphical views of said anatomical portion, whereinsaid generated plurality of multi-dimensional graphical views comprisesone or more of: a first set of views comprising said identified skullstructure information associated with said one surface layer; and asecond set of views comprising said identified skull structureinformation together with underlying tissue information and/or vesselinformation that corresponds to said other surface layers.